In the 16th century, hysteria reigned around a new technology that threatened to be “confusing and harmful” to the mind. The cause of such concern? The widespread availability of books brought about by the invention of the printing press [1]. In the early 19th century, concerns about schooling “exhausting the children’s brains” followed, with the medical community accepting that excessive study could be a cause of madness [2]. By the 20th century, the invention of the radio was accompanied by assertions that it would distract children from their reading (which by this point was no longer considered confusing and harmful) leading to impaired learning [3].
Today, the same arguments that were once leveled against reading, schooling, and radio are being made about screen use (e.g., television, mobile phones, and computers) [4]. Excessive screen time use is the number one concern parents have about their children’s health and behaviour, ahead of nutrition, bullying, and physical inactivity [5]. Yet, the evidence to support parents’ concerns is inadequate. A Lancet editorial [6] suggested that, “Our understanding of the benefits, harms, and risks of our rapidly changing digital landscape is sorely lacking.”
While some forms of screen use (e.g., television viewing) may be detrimental to health and wellbeing [7,8], evidence for other forms of screen exposure (e.g., video games or online communication, such as Zoom) remains less certain and, in some cases, may even be beneficial [9,10]. Thus, according to a Nature Human Behaviour editorial, research to determine the effect of screen exposure on youth is “a defining challenge of the digital age” [11]. With concerns over the impact of screen use including education, health, social development, and psychological well-being, a broad overview that identifies potential benefits and risks is needed.
Citing the negative effects of screens on health (e.g., increased risk of obesity) and health-related behaviours (e.g., sleep), guidelines from the World Health Organisation [12] and numerous government agencies [13,14] and statements by expert groups [15] have recommended that young people’s time spent using electronic media devices for entertainment purposes should be limited. For example, the Australian Government guidelines regarding sedentary behaviour recommend that young children (under the age of two) should not spend any time watching screens. They also recommend that children aged 2-5 years should spend a maximum of one hour engaged in recreational sedentary screen use per day, while children aged 5-12 and adolescents should spend no more than two hours. In contrast, some recent evidence suggests that exposure to electronic entertainment media that exceeds these guidelines (e.g., 3-4 hours per day) may not have meaningful adverse effects on children’s behaviour or mental health, and might, in fact, benefit their well-being, as long as this exposure does not reach extreme levels (e.g., 7 hours per day)[16]. Some research also indicates that content (e.g., video games vs television programs) plays an important role in determining the potential benefit or harm of youths’ exposure to screen-based media [17]. Indeed, educational screen time is positively related to educational outcomes [18]. This evidence has led some researchers to argue that a more nuanced approach to screen time guidelines is required [19].
In 2016, the American Academy of Pediatrics used a narrative review to examine the benefits and risks of children and adolescents’ electronic media [20] as a basis for updating their guidelines about screen use [15]. Since then, a large number of systematic reviews and meta-analyses have provided evidence about the potential benefits and risks of screen use. Yet, no review has synthesised the evidence available across a broad range of outcome domains, such as physical health, education, development, behaviour, and well-being.
In order to crystallise the evidence base and support further evidence-based guideline development and refinement, we reviewed published meta-analyses examining the effects of screen use on children and youth. This review synthesises evidence on any plausible outcome of electronic media exposure. Adopting this broad approach allowed us to provide a holistic perspective on the influence of screens on children’s lives. By synthesising across life domains (e.g., school and home), this review provides evidence to support better guidelines and advice for parents, teachers, pediatricians and other professionals in order to maximise human functioning.
Population: To be eligible for inclusion, meta-analyses needed to include meta-analytic effect sizes for children or adolescents (age 0-18 years). We included meta-analyses containing studies that combined data from adults and youth if meta-analytic effect size estimates specific to participants aged 18 years or less could be extracted (i.e., the highest individual study from the meta-analysis had a mean age was < 18 years). We excluded meta-analyses that only contained evidence gathered from adults (age >18 years).
Exposure: We included meta-analyses examining all types of electronic screens including (but not necessarily limited to) television, gaming consoles, computer, tablet, and mobile phones. We also included analyses of all types of content on these devices, including (but not necessarily limited to) recreational content (e.g., television programs, movies, games), homework, and communication (e.g., video chat). In this review we adopted a population-level perspective, meaning that we examined electronic media exposure that occurs during typical daily living activities (e.g., home, school-based electronic media exposure). Consistent with this population-level approach, we excluded technology-based treatments for clinical conditions. However, we included studies examining the effect of screen exposure on non-clinical outcomes (e.g., learning children) for children and youth with a clinical condition. For example, a meta-analysis of the effect of television watching on learning among adolescents diagnosed with depression would be included. However, a meta-analysis of interventions designed to treat clinical depression delivered by a mobile phone app would be excluded.
Outcomes: We included all reported outcomes.
Publications: We included meta-analyses (or meta-regressions) of quantitative evidence. To be included, meta-analyses needed to analyse data from studies identified in a systematic review. For our purposes, a systematic review was one in which the authors attempted to acquire all the research evidence that pertained to their research question(s). We excluded meta-analyses that did not attempt to summarise all the available evidence (e.g., a meta-analysis of all studies from one laboratory). We included meta-analyses regardless of the study designs included in the review (e.g., laboratory-based experimental studies, randomised controlled trials, non-randomised controlled trials, longitudinal, cross-sectional, case studies), as long as the studies in the review collected quantitative evidence. We excluded systematic reviews of qualitative evidence. We did not formulate inclusion/exclusion criteria related to the risk of bias of the review. We did, however, employ a risk of bias tool to help interpret the results. We included full-text, peer-reviewed meta-analyses published or ‘in-press’ in English. We excluded conference abstracts and meta-analyses that were unpublished.
We searched records contained in the following databases: Pubmed, MEDLINE, CINAHL, PsycINFO, SPORTDiscus, Education Source, Embase, Cochrane Library, Scopus, Web of Science, ProQuest Social Science Premium Collection, and ERIC. We conducted an initial search on August 17, 2018 and refreshed the search on May 13, 2020. We searched reference lists of included papers in order to identify additional eligible meta-analyses. We also searched PROSPERO to identify relevant protocols and contacted authors to determine if these reviews have been completed and published.
The search strategy associated with each of the 12 databases can be found here. We hand searched reference lists from any relevant umbrella reviews to identify systematic meta-analyses that our search may have missed.
Using Covidence software (Veritas Health Innovation, Melbourne, Australia), two researchers independently screened all titles and abstracts. Two researchers then independently reviewed full-text articles. We resolved disagreements at each stage of the process by consensus, with a third researcher employed, when needed.
From each included meta-analysis, two researchers independently extracted data into a custom-designed database.
From each meta-analysis we extracted the following items: First author, year of publication, study design restrictions (e.g., cross-sectional, observational, experimental), region restrictions (e.g., specific countries), earliest and latest study publication dates, sample age (mean), lowest and highest mean age reported, outcomes reported, and exposures reported.
For each meta-analysis, two researchers independently completed the National Health, Lung and Blood Institute’s Quality Assessment of Systematic Reviews and Meta-Analyses tool [21] (see Table 1). We resolved disagreements by consensus, with a third researcher employed when needed. We did not assess risk of bias in the individual studies that were included in each meta-analysis.
Two researchers independently extracted all quantitative meta-analytic effect sizes, including moderation results. Where possible, they also extracted effect sizes from primary studies included in each meta-analysis. To facilitate comparisons, we converted effect sizes to Pearson’s \(r\) using established formulae [22–24]. We excluded relative risk ratios from this conversion because meta-analyses did not contain sufficient information to meaningfully convert. Effect sizes on the original metric are provided in Supplementary File 1.
After extracting data, we examined the combinations of exposure and outcomes and removed any effects that appeared more than once, keeping the effect with the largest total sample size. In instances where effect sizes from the same combination of exposure and outcome were drawn from different populations (e.g., children vs adolescents) we retained both estimates in our dataset.
We excluded effect size estimates when the authors did not provide a sample size. We descriptively present the remaining meta-analytic effect sizes. To remove the differences in approach to meta-analyses across the reviews, we reran the effect size estimate using a random effects meta-analysis via the metafor package [25] in R [26] (version 4.1.2) when the meta-analysis’s authors provided primary study data associated with these effects. When required, we imputed missing sample sizes using mean imputation from the other studies within that review. From our reanalysis we also extracted \(I^2\) values. To test for publication bias, we conducted Egger’s test [27] when the number of studies within the review was ten or more [28], and conducted a test of excess significance [29]. We contacted authors who did not provide primary study data in their published article. Where authors did not provide data in a format that could be re-analysed, we used the published results of their original meta-analysis.
Statistical Credibility. We employed a statistical classification approach to grade the statistical credibility of the effect sizes in the literature. To be considered credible an effect needed to be derived from a combined sample of >1,000 [30] and have non-significant tests of publication bias (i.e., Egger’s test and excess significance test). We performed these analyses, and therefore the review needed to provide usable study-level data in order to be included.
Consistency of Effect within the Population. We also examined the consistency of the effect size using the \(I^2\) measure. We considered \(I^2 < 50\%\) to indicate effects that were relatively consistent across the population of interest. \(I^2\) values of \(> 50\%\) were taken to indicate an effect was potentially heterogeneous within the population.
Direction of Effect. Finally, we examined the extent to which significance testing suggested screen exposure was associated with benefit, harm, or no effect on outcomes. We used thresholds of \(P < .05\) for weak evidence and \(P < 10^{-3}\) for strong evidence. An effect that was neither significant at \(P < .05\) or \(10^{-3}\) that also passed the criteria for statistical credibility was taken to indicate no association of interest.
We initially planned to include systematic reviews without meta-analyses in a narrative summary alongside the main meta-analytic findings. However, we determined that combining results from the meta-analyses provided a robust overview. Readers interested in the relevant systematic reviews (i.e., without meta-analysis) can consult the list of references in Supplementary File 2.
We altered our evidence assessment plan when we identified that, as written, it would treat effects from large reviews, with limited evidence of publication bias, and with consistency of effect in the population as not credible if the effect size was not statistically significant. This would have significantly harmed knowledge gained from our review as it would have restricted our ability to show where the empirical evidence strongly indicated that there was no association between screen time and a given outcome.
The searches yielded 41,928 results, of which 24,023 were duplicates. After screening titles and abstracts, we assessed 1,964 full-texts for inclusion. One hundred and seventeen met the inclusion criteria and we extracted the data from these meta-analyses. Figure 1 presents the full results of the selection process.
Figure 1: PRISMA Diagram
The most frequently reported exposures were general tv programs and movies (n = 26), physically active video games (n = 15), screen-based lifestyle risk behaviour interventions (at school) (n = 14), and general screen use (n = 13). Supplementary File 3 provides a list of all exposures identified. The most frequently reported outcomes were body composition (n = 34), general physical activity (n = 15), general literacy (n = 13), general learning (n = 12), and duration sleep (n = 9). In most cases (121/197), there was only one exposure/outcome combination for an age group, with 20 appearing twice, and 8 appearing three or more times. Full characteristics of the included studies are provided in Table 1. Our process yielded 167 unique effect/outcome combinations contributed from 51 reviews. These effects represent the findings of 2,171 primary studies comprised of 1,652,944 participants.
| Study Characteristics | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Review characteristics and quality assessment for studies providing unique effects | ||||||||||||||
| Review Characteristics | Quality Assessment1 | |||||||||||||
| First Author | Year | Design Restrictions | Regions Restrictions | Study Range Earliest - Latest |
Sample Age Restrictions (Age Range)2 |
Outcomes Assessed | Exposures Assessed | Elig. Crit.3 |
Lit. Search4 | Dual Screening5 | Dual Quality6 | Studies Listed7 | Pub. Bias Assessed8 | Heterog. Assessed9 |
| Abrami | 2015 | Include: Experimental; Quasi-experimental | None specified | 2009–2015 | School-age Children (Early Primary/Elementary) |
|
|
High | High | Low | High | Low | Low | Low |
| Adelantado-Renau | 2019 | Include: Cross-sectional studies | None specified | 1982–2019 | Children; Adolescents (5.7-18.0) |
|
|
Low | Low | Low | Low | Low | Low | Low |
| Aghasi | 2020 | Include: Observational | None specified | 2007–2016 | All (14.2-15.8) |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Andrade | 2019 | Include: Interventions | None specified | 2010–2017 | Children; Adolescents |
|
|
Unclear | Low | Low | Unclear | Low | High | Low |
| Bartel | 2015 | None | None specified | 2004–2014 | Adolescents (12.2-17.7) |
|
|
Low | Low | Unclear | Unclear | Low | Unclear | Unclear |
| Blok | 2002 | None | None specified | 1990–2000 | All (5.4-11.5) |
|
|
Unclear | Low | High | High | Low | High | Low |
| Bossen | 2020 | Include: Randomised controlled trials | None specified | 2011–2018 | Children (10.0-15.7) |
|
|
Unclear | Low | Low | Low | Low | High | Low |
| Boyland | 2016 | Include: Experimental | None specified | 2004–2015 | Children; Adolescents (6.0-10.4) |
|
|
High | Low | Low | Unclear | Low | Low | Low |
| Byun | 2018 | Include: All quantitative designs | None specified | 2006–2014 | School-age Children |
|
|
Unclear | Unclear | Unclear | High | High | High | High |
| Carter | 2016 | Include: All quantitative designs | None specified | 2011–2015 | Children; Adolescents (10.6-17.0) |
|
|
Low | Low | Unclear | Low | Low | High | Low |
| Champion | 2019 | Include: Randomised controlled trials | None specified | 2003–2017 | School-age Children (11.4-15.9) |
|
|
Low | Low | Low | Low | Low | Low | Low |
| Chan | 2014 | Include: Experimental; Quasi-experimental | None specified | 2002–2012 | School-age Children |
|
|
Unclear | High | High | High | Low | Low | Low |
| Cheung | 2012 | Include: Randomised controlled trials | None specified | 1982–2010 | School-age Children |
|
|
Unclear | Low | Low | High | High | Low | Low |
| Cheung | 2013 | Include: Experimental; Quasi-experimental | None specified | 1980–2010 | School-age Children |
|
|
Low | High | High | Unclear | Low | Low | Low |
| Coyne | 2018 | None | None specified | 1975–2017 | Children; Adolescents |
|
|
Low | Low | Low | High | Low | Low | Low |
| Cushing | 2010 | Include: All quantitative designs; Experimental | None specified | 1989–2009 | Children; Adolescents |
|
|
Unclear | Low | High | High | Low | Low | Low |
| Darling | 2017 | Include: Intervention | None specified | 2006–2016 | NA (8.7-16.0) |
|
|
Unclear | Low | Unclear | Unclear | Low | High | High |
| de Oliveira | 2016 | Include: Observational | None specified | 2010–2014 | Adolescents |
|
|
Low | Low | Low | Low | Low | Low | Low |
| Fang | 2019 | Include: Cohort; Case-control; Cross-sectional | None specified | 2006–2019 | Children; Adolescents |
|
|
Unclear | Low | Low | Low | Low | Low | Low |
| Ferguson | 2017 | None | None specified | 2005–2017 | Children; Adolescents |
|
|
Unclear | Low | Low | High | Low | Low | Low |
| Folkvord | 2018 | Include: Interventions | None specified | 2007–2018 | Children; Adolescents |
|
|
Unclear | Low | Low | Unclear | Low | High | Low |
| Gardella | 2017 | Include: Cross-sectional | Include: North America | 2006–2014 | Adolescents (12.5-16.2) |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Ghobadi | 2018 | Include: Cohort; Case-control; Cross-sectional Exclude: Interventions | None specified | 2009–2014 | Children; Adolescents |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Graham | 2015 | Include: Experimental; Quasi-experimental | None specified | 2004–2011 | School-age Children (Primary/Elementary/Middle School) |
|
|
Unclear | Low | High | High | Low | Low | Low |
| Hammersley | 2016 | Include: Randomised controlled trials | None specified | 2003–2013 | Children; Adolescents |
|
|
Low | Low | High | Low | Low | High | Low |
| Hassan-Saleh | 2019 | Include: Experimental; Quasi-experimental | None specified | 2008–2016 | Children; Adolescents |
|
|
Unclear | Low | Unclear | Unclear | High | High | Low |
| Hernandez-Jimenez | 2019 | Include: Experimental; Quasi-experimental | None specified | 2009–2017 | Children; Adolescents |
|
|
Unclear | Low | High | Low | Low | Low | Low |
| Hurwitz | 2018 | None | Include: North America | 1997–2018 | Early childhood/pre-school; School-age Children (Early Primary/Elementary) |
|
|
Low | Low | High | High | Low | Low | Low |
| Janssen | 2020 | Include: Experimental; Cross-sectional; Longitudial | None specified | 2007–2019 | Children |
|
|
Unclear | Low | Low | Low | Low | Unclear | Low |
| Kates | 2018 | None | None specified | 2008–2016 | School-age Children |
|
|
Unclear | High | Low | High | High | Low | Low |
| Kroesbergen | 2003 | Include: Within subject design; between subject design | None specified | 1985–1999 | School-age Children (Primary/Elementary) (7.0-11.3) |
|
|
Unclear | Low | Unclear | High | Low | High | Low |
| Kucukalkan | 2019 | Include: Experimental | None specified | 2007–2016 | School-age Children (Primary/Elementary) |
|
|
Unclear | Low | Unclear | Unclear | High | Low | Low |
| Lanca | 2020 | Include: Cohort; Case-control; Cross-sectional; Intervention trials. Exclude: Case reports; Retrospective studies. | None specified | 2007–2016 | Children; Adolescents |
|
|
Unclear | Low | High | Unclear | Low | Low | Low |
| Li | 2010 | Include: Experimental; Quasi-experimental | None specified | 1991–2005 | School-age Children |
|
|
Unclear | Low | Low | Unclear | Low | High | Low |
| Liao | 2008 | Include: All quantitative designs | Include: Taiwan | 1990–2003 | School-age Children (Primary/Elementary) |
|
|
Low | High | High | Low | High | High | High |
| Liao | 2014 | Include: Randomised controlled trials (no treatment controls) | None specified | 1999–2012 | Children; Adolescents (4.0-14.7) |
|
|
Unclear | Low | High | Low | Low | Low | Low |
| Liu | 2016 | Include: Cross-sectional; Case-control; Longitudinal | None specified | 2001–2014 | All (10.9-18.0) |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Liu | 2019 | Include: All quantitative designs | None specified | 2007–2014 | All (13.3-16.6) |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| Madigan | 2020 | Include: Observational Exclude: Qualitative | None specified | 1973–2019 | Children (0.5-10.6) |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Mares | 2005 | None | None specified | 1969–1989 | Children |
|
|
Unclear | Low | High | High | Low | High | High |
| Mares | 2013 | Exclude: Experimental | Exclude: North America | 1973–2010 | Children |
|
|
Unclear | High | High | High | Low | High | Low |
| Marshall | 2004 | None | None specified | 1985–2002 | Children; Adolescents |
|
|
Unclear | Low | High | High | High | High | Low |
| Martins | 2019 | Include: All quantitative designs | None specified | 2003–2018 | All |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| McArthur | 2012 | Include: Randomised controlled trials and quasi-RCTs. | None specified | 1994–2009 | All (6.7-16.2) |
|
|
Low | Low | Low | Low | Low | Low | Low |
| McArthur | 2018 | Include: Randomised controlled trials and quasi-RCTs. | Include: English speaking countries | 1994–2015 | Children; Adolescents |
|
|
Low | Low | Low | Low | Low | Low | Low |
| Oldrati | 2020 | Include: Group-control experimental design | None specified | 2006–2018 | School-age Children |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| Paik | 1994 | None | None specified | NA | Children; Adolescents |
|
|
Unclear | High | Unclear | High | High | Low | High |
| Pearce | 2016 | Include: All quantitative designs | None specified | 1986–2012 | Children; Adolescents |
|
|
Unclear | Low | High | High | High | Low | Low |
| Peng | 2011 | None | None specified | 2001–2010 | Children; Adolescents |
|
|
Unclear | Low | Unclear | Unclear | Low | High | Low |
| Poorolajal | 2020 | Include: Observational | None specified | 1995–2018 | Children; Adolescents |
|
|
Unclear | Low | Low | Unclear | Low | Low | Low |
| Prescott | 2018 | Include: Longitudinal | None specified | 2008–2017 | All (8.9-16.0) |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| Rodriguez-Rocha | 2019 | Include: Experimental; Quasi-experimental | None specified | 1999–2018 | All (4.5-15.4) |
|
|
Unclear | Low | Low | Low | Low | Low | Low |
| Sadeghirad | 2016 | Include: Randomised controlled trials | None specified | 1978–2014 | Children; Adolescents (3.8-11.0) |
|
|
High | Low | Low | Low | Low | Low | Low |
| Schroeder | 2013 | Include: Experimental; Quasi-experimental | None specified | 2001–2009 | All |
|
|
Low | Low | Unclear | High | Low | Low | Low |
| Scionti | 2019 | Include: Interventions | None specifed | 2009–2019 | Children (4.3-6.1) |
|
|
Low | Low | Low | High | Low | Low | Low |
| Shin | 2019 | Include: Interventions | None specified | 2013–2018 | Children; Adolescents (12.0-15.7) |
|
|
Unclear | Low | Low | Low | Low | High | Low |
| Slavin | 2014 | Include: Randomised controlled trials; Quasi-experimental; Observational | None specified | 2000–2011 | School-age Children (Primary/Elementary) |
|
|
Unclear | High | High | High | Low | High | High |
| Takacs | 2014 | Include: Experimental; Quasi-experimental | None specified | 1980–2014 | NA |
|
|
High | Low | Unclear | High | Low | Low | Low |
| Takacs | 2019 | Include: Randomised controlled trials and quasi-RCTs. | None specified | 2001–2016 | Children |
|
|
Low | Low | Unclear | High | Low | Low | Low |
| Tekedere | 2016 | None | None specified | 2010–2015 | All |
|
|
Unclear | High | Unclear | Unclear | Low | Low | Low |
| Vahedi | 2018 | Include: Interventions (pre-post or controlled). Exclude: Cross-sectional | None specified | 2015–2016 | School-age Children (Middle/High School) |
|
|
Low | Low | Unclear | Unclear | Low | Low | Low |
| Vannucci | 2020 | Exclude: Qualitative; Case studies | None specified | 2011–2018 | Adolescents (12.6-18.0) |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| Xie | 2018 | Include: Experimental; Quasi-experimental; Pre-test post-test | None specified | 2010–2018 | Children (1.8-5.9) |
|
|
Unclear | Low | Low | High | Low | Low | Low |
| Zhang | 2016 | Include: Cohort; Case-control; Cross-sectional | None specified | 2001–2014 | Children |
|
|
Unclear | Low | Low | High | Low | Low | Low |
| Zhou | 2020 | Exclude: Non-empirical studies; Qualitative; Systematic reviews or meta-analyses | None specified | 2009–2018 | All |
|
|
Unclear | Low | Unclear | High | Low | Low | Low |
| Zucker | 2009 | Include: Randomised controlled trials; Quasi-experimental; Observational | None specified | 1997–2006 | School-age Children (Primary/Elementary) |
|
|
Low | Low | Unclear | High | Low | High | Low |
|
1
Items are from the National Health, Lung and Blood Institute’s Quality Assessment of Systematic Reviews and Meta-Analyses tool. Note that we excluded the first item of the tool.
2
Where provided
3
Eligibility criteria predefined and specified
4
Literature search strategy comprehensive and systematic
5
Dual independent screening & review
6
Dual independent quality assessment
7
Included studies listed with important characteristics and results of each
8
Publication bias assessed
9
Heterogeneity assessed
|
||||||||||||||
The quality of the included meta-analyses was mixed (see Table 1). Most assessed heterogeneity (n low risk = 59/66, 89% of meta-analyses), reported the characteristics of the included studies (n low risk = 57/66, 86%), and used a comprehensive and systematic search strategy (n low risk = 56/66, 85%). Most reviews did not clearly report if their eligibility criteria were predefined (n unclear = 45/66, 68%). Many papers also did not complete dual independent screening of abstracts and full text (n high risk = 16/66, 24%) or did not clearly report the method of screening (n unclear = 21/66, 32%). A similar trend was observed for dual independent quality assessment (n high risk = 31/66, 47%; n unclear = 19/66, 29%). Overall, only 5 meta-analyses were graded as low risk of bias on all criteria. Zero meta-analyses showed high risk of bias on all criteria (if ‘unclear’ was treated as ‘high’ then one meta-analysis would be considered all ‘high risk’).
There were 46 unique effects associated with education outcomes, including general learning outcomes, literacy, numeracy, and science. We removed 20 effects that did not provide individual study-level data, 7 effects with samples < 1,000, and 8 effects with a significant Egger’s test or insufficient studies to conduct the test. Effects not meeting one or more of these standards are presented in Supplementary File 4. The remaining 12 effects met our criteria for statistical credibility and are described in Figure 2. These 12 effects came from 8 meta-analytic reviews analysing data from 226 empirical studies with 186,631 individual participants.
Figure 2: Education outcomes
Among the statistically credible effects, general screen use, television viewing, and video games were all negatively associated with learning. E-books that included narration, as well as touch screen education interventions, and augmented reality education interventions were positively associated with learning. General screen use was negatively associated with literacy outcomes. However, if the screen use involved co-viewing (e.g., watching with a parent), or the content of television programs was educational, the association with literacy was positive (95% CI, weak evidence). Numeracy outcomes were positively associated with screen-based mathematics interventions and video games that contained numeracy content.
As shown in Figure 2, most of the credible results (10 of 12 effects) showed statistically significant associations, with 99.9% confidence intervals not encompassing zero (strong evidence). The remaining two associations were significant at the 95% confidence level (weak evidence). All credible effects related to education outcomes were small-to-moderate. Screen-based interventions designed to influence an outcome (e.g., a computer based program designed to enhance learning [31]) tended to have larger effect sizes than exposures that were not specifically intended to influence any of the measured outcomes (e.g., the association between television viewing and learning [32]). The largest effect size observed was for augmented reality-based education interventions on general learning (\(r = 0.33, k = 15, N = 1,474\)). Most effects showed high levels of heterogeneity (10 of 12 with \(I^2 > 50\%\)).
We identified 121 unique outcome-exposure combinations associated with health or health-related behaviour outcomes. We removed 33 effects that did not provide individual study-level data, 30 effects with samples < 1,000, and 43 effects with a significant Egger’s test or insufficient studies to conduct the test. No remaining studies showed evidence of excessive significance. Effects not meeting one or more of these standards are presented in Supplementary File 5. The remaining 17 meta-analytic associations met our criteria for credible evidence and are described below (see also Figure 3). These 121 effects came from 12 meta-analytic reviews analysing data from 231 empirical studies with 676,331 individual participants.
Figure 3: Health and health-related behaviour outcomes
Digital advertising of unhealthy foods—both traditional advertising and video games developed by a brand for promotion—were associated with higher unhealthy food intake. Social media use and sexy media exposure were positively associated with risky behaviors (e.g., sexual activity, risk taking, and substance abuse). General screen use was positively associated with depression. Television viewing was negatively correlated with sleep duration, but only at the 95% confidence level (weak evidence). All forms of screen use (general, television, and video games) were positively associated with body composition (e.g., BMI), although the association was smaller for children than for adolescents or for combined populations. Screen-based interventions which target health behaviours appeared effective.
Across the health outcomes, most (14 of 17) effects were statistically significant at the 99.9% confidence interval level, with the remaining three significant at 95% confidence. However, most of the credible effects exhibited high levels of heterogeneity, with all but one having \(I^2 > 75\%\). Additionally, most effects were small, with the association between unhealthy food advertising and intake the largest at \(r = 0.25\) (\(k = 13, N = 1,756\)). Most of the effect sizes (14/17) had an absolute value of \(r < 0.2\).
The primary goal of this review was to provide a holistic perspective on the influence of screens on children’s lives across a broad domain of outcomes. We found that when meta-analyses examined general screen use, and did not specify the content, context or device, there was strong evidence showing potentially harmful associations with general learning, literacy, body composition, and depression. However, when meta-analyses included a more nuanced examination of exposures a more complex picture appeared.
Consider children watching television programs—an often cited form of screen time harm. Indeed, we find robust evidence for a small association with poorer academic performance and literacy skills [32]. However, we also found evidence that if the content of the program was educational, or the child was watching the program with a parent (i.e., co-viewing), this exposure was instead associated with better literacy [33]. Thus, parents may play an important role in selecting content that is likely to benefit their children or, perhaps, interact with their children in ways that may foster literacy (e.g., asking their children questions about the program). Similar nuanced findings occur for video games. The credible evidence we identified showed that video game playing is associated with poorer body composition and learning [32,34]. However, when the video game was designed specifically to teach numeracy, playing these games showed learning benefits [35]. One might expect that video games designed to be physically active could confer health benefits, but none of the meta-analyses examining this hypothesis met our thresholds for statistical credibility (see Supplementary Files 4 & 5) therefore this hypothesis could not be addressed.
Social media was one type of exposure that showed consistent risks to health, with no indication of potential benefit. Social media showed strong evidence of harmful associations with risk taking in general, as well as unsafe sex and substance abuse [36]. These results align with meta-analytic evidence from adults indicating that social media use is also associated with increased risk of depression [37,38]. Recent evidence from social media companies themselves suggest there may also be negative effects of social media on the mental health of young people, especially teenage girls [39].
One category of exposure appeared to consistently confer benefits: screen-based interventions designed to promote learning or health behaviours. This finding indicates that interventions can be effectively delivered using electronic media platforms, but does not necessarily indicate that screens are more effective than other methods (e.g., face-to-face, printed material). Rather, it reinforces that the content of the screen time may be the most important aspect. The way that a young person interacts with digital screens may also be important. We found evidence that touch screens had strong evidence for benefits on learning [31], as did augmented reality [40].
Largely owing to a small number of studies or missing individual study data, there were few age-based conclusions that could be drawn from reviews which met our criteria for statistical certainty. If we expand to include those reviews which did meet this threshold, there remains no clear pattern although there were some age-specific differences in associations. For example, advertising of unhealthy food was associated with unhealthy food choice for young children, but was not statistically significant for other age groups [41]. Conversely, TV programs and movies were more strongly associated with lower physical activity for adolescents than for younger age groups [42].
Broadly, our findings align with the recommendations of others who suggest that current guidelines may be too simplistic, mischaracterise the strength of the evidence, or do not acknowledge the important nuances of the issue [43–45]. Our findings suggest that screen use is a complex issue, with associations based not just on duration and device type, but also on the content and the environment in which the exposure occurs. Many current guidelines simplify this complex relationship as something that should be minimised in all instances. We suggest that future guidelines need to embrace the complexity of the issue, to give parents and clinicians specific information to weigh the pros and cons of interactions with screens.
Screen use research is big, varied, and rapidly growing. Reviews tended to be general (e.g., all screen time) and even when more targeted (e.g., social media) nuances related to specific content (e.g., Instagram vs Facebook) have not been meta-analysed or have not produced credible evidence.
Screen time research has a well-established measurement problem, which impacts the individual studies of this umbrella review. The vast majority of screen time research relies on self-reported data, which not only lacks the nuance required for understanding the effects of screen time, but may also be inaccurate. In a meta-analysis of 47 studies comparing self-reported media use with logged measures, Parry et al [46] found that the measures were only moderately correlated (\(r = 0.38\)), with self-reported problematic usage fairing worse (\(r = 0.25\)). It may be that the field of screen time research cannot be sufficiently advanced until more accurate and nuanced measures are available.
Our primary goal for this umbrella review was to provide a high-level synthesis of screen time research, by examining a range of exposures and the associations with a broad scope of outcomes. Our results represent the findings from 2,171 primary studies comprised of 1,652,944 participants. To ensure findings could be compared on a common metric, we extracted and reanalysed individual study data where possible.
Our high-level approach limits the feasibility of examining fine-grained details of the individual studies. For example, we did not examine moderators beyond age, nor did we rate the risk of bias for the individual studies. Thus, our assessment of evidence quality was restricted to statistical credibility, rather than a more complete assessment of quality (e.g., GRADE [47]). As such, we made decisions regarding the credibility of evidence, where others may have used different thresholds or metrics. For this reason, we provide the complete results in the supplementary material, along with the dataset for others to consider alternative criteria.
Screen time is a topic of significant interest, as shown by the wide variety of academic domains involved and the growing pervasiveness into society. Our findings showed that the impact of screen time can be both positive (e.g., educational video games were associated with improved literacy) and negative (e.g., general screen use was associated with poorer body composition). The interplay of these findings show that parents, teachers, and other caregivers need to carefully weigh the pros and cons of each specific activity for potential harms and benefits. However, our findings also suggest that in order to aid caregivers to make this judgement, researchers need to conduct more careful and nuanced measurement and analysis of screen time, with less emphasis on measures that aggregate screen time and instead focus on the content, context, and environment in which the exposure occurs.
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